Plotly’s animation features let you turn a static time series into an evolving story where the plot moves frame by frame. An animated line chart is especially useful when you want to highlight how trends develop over time or how multiple series diverge from a common starting point. Instead of providing several static snapshots, you can give the user a slider and a play button to explore the timeline interactively.

Box plots and violin plots are statistical summaries that reveal distribution shape, central tendency, and outliers. When you add Plotly’s interactivity, these plots become powerful exploration tools where viewers can hover for details, zoom into specific ranges, and compare multiple groups. Unlike static statistical plots, interactive Plotly visualizations invite exploration and deeper understanding.

Plotly allows you to create interactive, publication-quality figures in Python. You can customize the width and height of your figures through multiple approaches: using update_layout, Plotly Express parameters, default templates, or configuration options when exporting. This guide covers each method with code examples.

In Plotly, a trace represents a single dataset or graphical element within a chart. Each trace is an object that defines how data is displayed—such as lines, bars, markers, or other shapes. You can use multiple traces to layer and customize complex plots.

Plotly is renowned for its interactive web-based plots, but you often need to save these plots as static image files (PNG, JPEG, SVG) or as interactive HTML files for sharing or embedding. See how to save your Plotly figures to local files.

When creating plots, especially scatter plots or scientific visualizations, it’s often crucial to ensure that the x and y axes have the same scale. This means that a unit of distance on the x-axis represents the same magnitude as a unit of distance on the y-axis. In Plotly, this is achieved by setting the aspect ratio of the plot. See how to set the same scale for both axes in Plotly.

Plotly is a versatile charting library that allows for extensive customization. One common requirement is to make the background of a plot transparent, which is particularly useful when embedding plots on websites with custom backgrounds or when layering plots. See how to set both the plot area and the overall figure background to transparent in Plotly.

Plotly’s subplot functionality is incredibly useful for displaying multiple related plots in a single figure. By default, make_subplots adds a certain amount of spacing between these plots for readability. However, there are many cases where you might want to reduce or completely remove this space to create a more compact or visually continuous layout. See how to control the spacing between subplots in Plotly. Understanding Subplot Spacing

Vertical lines (vlines) are a great way to highlight specific x-values on a Plotly chart—whether it’s a threshold, a special event, or a point of interest. Learn how to use Plotly to create vertical lines in both Plotly Express and Graph Objects.

Plotly is a powerful Python library for creating interactive plots. By default, Plotly displays information about data points when you hover over them. While this is useful in many cases, sometimes you might want to disable this hover behavior for specific traces or even the entire plot. See how to achieve this.

Controlling the range of the y-axis in Plotly is essential for effectively visualizing your data. This is how to set the y-axis range for various plot types in Plotly.

The legend in Plotly provides information about the traces in your plot. While often useful, there are situations where you might want to hide the legend. Check several ways to hide the legend in Plotly.

Plotly offers a wide range of options for customizing the appearance of your plots, including the color of lines. This is how to change line color in Plotly for various types of plots.

Plotly is a versatile Python library for creating interactive data visualizations. One of the most commonly used visualization types is the scatter plot, which allows you to visualize the relationship between two numerical variables. We’ll explore how to create interactive scatter plots using Plotly in Python, enabling you to explore and analyze your data with ease.